Front Matter

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Objective

R is a great data analysis and stats language and has lots of practical applications in many businesses. Using it can result in a fantastic return on investment as R is free to use, but you’ll only get that ROI when you’re using it in a robust infrastructure and utilising sensible development practices. This training day takes you through the basics of R development, showing you the best practices along the way, then shows you how to setup the Linux infrastructure needed to do team development and to deliver reporting across the company.

itsalocke.com | github.com/stephlocke | @SteffLocke

R

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About R

R is an integrated suite of software facilities for data manipulation, calculation and graphical display

How it hangs together

Top 10 packages

  1. data.table
  2. ggplot2
  3. knitr
  4. shiny
  5. rmarkdown
  6. RODBC
  7. magrittr
  8. stringr
  9. dplyr
  10. httr

Installation

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R

  • Download latest base R exe file from cran.rstudio.com
  • Also download Rtools latest exe
  • Install R then Rtools - if 64bit, install both 32 and 64 as it saves you hassle with other drivers

Rstudio

  • Go to rstudio.com
  • Select correct install file for your PC, and install

Git

Configuration

  • Open Rstudio
  • Go to Tools> global options and set rstudio to point at the location of your git.exe file Git exe location
  • Setup your details in git by going to git > shell in Rstudio or opening up command-prompt
    • Run git config --global user.name="Your name"
    • Run git config --global user.email="email@addre.ss"
  • Use Packages > Install to install devtools and installr

  • Install Rtraining by executing

    library(devtools)
# Case-sensitive!
install_github("stephlocke/Rtaining")

Syntax & structure

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Basic operators

Action Operator Example
Subtract - 5 - 4 = 1
Add + 5 + 4 = 9
Multiply * 5 * 4 = 20
Divide / 5 / 4 = 1.25
Raise to the power ^ 5 ^ 4 = 625
Modulus %% 10 %% 4 = 2
Absolute remainder %/% 9 %/% 4 = 2
Basic sequence : sum(1:3) = 6

Comparison operators

Action Operator Example
Less than < 5 < 5 = FALSE
Less than or equal to <= 5 <= 5 = TRUE
Greater than > 5 > 5 = FALSE
Greater than or equal to >= 5 >= 5 = TRUE
Exactly equal == (0.5 - 0.3) == (0.3 - 0.1) is FALSE, 2 == 2 is TRUE
Not equal != (0.5 - 0.3) != (0.3 - 0.1) is TRUE, 2 != 2 is FALSE
Equal all.equal() all.equal(0.5 - 0.3,0.3 - 0.1) is TRUE

States

States Representation
True TRUE , 1
False FALSE , 0
Empty NULL
Unknown NA
Not a number e.g. 0/0 NaN
Infinite e.g. 1/0 Inf

Logical operators

Action Operator Example
Not ! !TRUE is FALSE
And & TRUE & FALSE is FALSE, c(TRUE,TRUE) & c(FALSE,TRUE) is FALSE, TRUE
Or | TRUE | FALSE is TRUE, c(TRUE,FALSE) | c(FALSE,FALSE) is TRUE, FALSE
Xor xor() xor(TRUE,FALSE) is TRUE
Bitwise And && c(TRUE,TRUE) && c(FALSE,TRUE) is FALSE
Bitwise Or || c(TRUE,FALSE) || c(FALSE,FALSE) is TRUE
In %in% “Red” %in% c(“Blue”,“Red”) = TRUE
Not in !( x %in% y) or Hmisc::%nin% “Red” %nin% c(“Blue”,“Red”) = FALSE

Control constructs

Type Implementation Example
If if(condition) {dosomething} if(TRUE) { 2 } returns 2
If else if(condition) {do something} else {do something different} or ifelse(condition, do something, do something else) if(FALSE) { 2 } else { 3 } returns 3, ifelse(FALSE, 2, 3) returns 3
For loop for(i in seq) {dosomething} or foreach::foreach(i=1:3) %do% {something} foreach(i=1:3) %do% {TRUE} returns TRUE, TRUE, TRUE
While loop while(condition) {do something } a<-0 ; while(a<3){a<-a+1} ; a returns 3
Switch switch(value, …) switch(2, “a”, “b”) returns b
Case memisc::cases(…) cases(“pi<3”=pi<3, “pi=3”=pi==3,“pi>3”=pi>3) returns pi>3

NB: If you find yourself using a loop, there’s probably a better, faster solution

Assignment operators

Action Operator Example
Create / update a variable <- a <- 10

NB: There are others you could use, but this is the best practice

Accessors

Action Operator Example
Use public function from package :: memisc::cases()
Use private function from package ::: optiRum:::pounds_format()
Get a component e.g a data.frame column $ iris$Sepal.Length
Extract a property from a class @ Won’t be used in this course
Refer to positions in a data.frame or vector [ ] iris[5:10,1]
Refer to item in a list [[ ]] list(iris=iris,mtcars=mtcars)[[“iris”]]

Data types

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Primitive data types

Data type Example
Integer 1
Logical TRUE
Numeric 1.1
String / character “Red”
Factor (enumerated string) “Amber” or 2 in c(“Red”,“Amber”,“Green”)
Complex i
Date “2015-04-24”

Compound data types

Data type Info Construction example(s)
Vector A 1D set of values of the same data type c(1,“a”) , 1:3 , LETTERS
Matrix A 2D set of values of the same data type matrix(LETTERS,nrow=13, ncol=2) , rbind(1:5,2:6)
Array An nD set of values of the same data type array(LETTERS, c(13,2))
Data.frame A 2D set of values of different data types data.frame(a=1:26, b=LETTERS)
List A collection of objects of various data types list(vector=c(1,“a”), df=data.frame(a=1:6))
Classes A class is like a formalised list and can also contain functions i.e. methods Won’t be covered in this class

NB: Most of my work uses vectors, data.tables (a souped up version of data.frames), and lists

Useful functions relating to data types

Function Use
is.[data type] Whether a vector is of a particular type
as.[data type] Attempts to coerce a vector to a data type
str Structure of an object including class/data type, dimensions
class The class(es)/data type(s) an object belongs to
summary Summarises an object
dput Get R code that recreates an object
unlist Simplify a list to a vector
dim Dimensions of a data type

IO

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In

Format Functions
CSV read.csv , data.table::fread , readr::read_csv
Excel readxl::read_excel
Database RODBC::sqlQuery , DBI::dbGetQuery
SPSS / SAS / Stata haven::read_[prog]
Hadoop rHadoopClient::read.hdfs
NoSQL mongodb::mongo.find , RNeo4Jj::getNodes

Out

Format Functions
CSV write.csv
Excel .
Database RODBC::sqlSave , DBI::dbGetQuery
SPSS / SAS / Stata .
Hadoop .
NoSQL .

Wrangling tables

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Requirements

  • necessary package: data.table (>=v1.9.5)
  • suggested package: optiRum

SQL

data.table is “SQL-like”

DT[i, j, by]
DT[WHERE | JOIN | ORDER, SELECT | UPDATE, GROUP]

data.table behaves like a database

A data.table acts like an in-memory RDBMS:

  • The result of a query is a table
  • Can set clustered primary keys over single or multiple columns
  • Can set non-clustered secondary keys over single or multiple columns
  • Can perform a wide range of relational algebra
  • Can do CRUD operations
  • Think in columns / sets not rows!

data.table differences to SQL Server

There are some differences that need to be mentioned:

  • A primary key can have repeat values
  • No constrained foreign keys
  • Joins are done by position in key so tblA with key A, and tblB with key C,A would join on tblA[A] and tblB[C]
  • It’s inherently dynamic so vectors of column names etc. can be provided
  • DELETE doesn’t exist explicitly
  • It’s case-sensitive

data.table cookbook

Single table basics

Task Generic syntax Example(s)*
CREATE data.table(…) data.table(a=1:3 , b=LETTERS[1:3]) data.table(iris)
PRIMARY KEY data.table(…,key) setkey() data.table(a=1:3 , b=LETTERS[1:3], key="b") setkey(data.table(iris),Species)
SELECT basic DT[ , .( cols )] irisDT[ , .(Species, Sepal.Length)]
SELECT alias DT[ , .( a=col )] irisDT[ , .(Species, Length=Sepal.Length)]
SELECT COUNT DT[ , .N] irisDT[ ,.N]
SELECT COUNT DISTINCT DT[ , uniqueN(cols)] irisDT[ ,uniqueN(.SD)]
SELECT aggregation DT[ , .( sum(col) , .N )] irisDT[ , .(Count=.N, Length=mean(Sepal.Length))]
SELECT dynamically i.e. by reference DT[ , colnames , with=FALSE] cols<-colnames(irisDT); irisDT[ , cols, with=FALSE]
WHERE exact on primary key DT[value] DT[value, ] irisDT["setosa"] irisDT["setosa", .(Count=.N)]
WHERE DT[condition] DT[condition, j, by] irisDT[Species=="setosa"] irisDT[Species=="setosa", .(Count=.N)]
WHERE BETWEEN DT[between(col, min, max)] DT[ col %between% c(min,max) ] irisDT[between(Sepal.Length, 1, 5)] irisDT[Sepal.Length %between% c(1,5)]
WHERE LIKE DT[like(col,pattern)] DT[ col %like% pattern ] irisDT[like(Species,"set")] irisDT[Species %like% "set"]
ORDER asc. DT[order(cols)] DT[order(cols), j, by] irisDT[order(Species)]
ORDER desc. DT[order(-cols)] DT[order(-cols), j, by] irisDT[order(-Species)]
ORDER multiple DT[order(cols)] DT[order(cols), j, by] irisDT[order(-Species, Petal.Width)]
GROUP BY single DT[i, j, by] irisDT[ ,.N, by=Species]
GROUP BY multiple DT[i, j, by] irisDT[ ,.N, by=.(Species,Width=Petal.Width)]
TOP head(DT, n) head(irisDT)
HAVING DT[i, j, by][condition] irisDT[ , .(Count=.N), by=Species][Count>25]
Sub-queries DT[…][…][…] irisDT[ , .(Sepal.Length=mean(Sepal.Length)), by=Species][Sepal.Length>6, .(Species)]

* Uses irisDT <- data.table(iris)

CRUD

Task Generic syntax Example(s)*
INSERT DT <- rbindlist(DT, newDT) irisDT<-rbindlist( irisDT, irisDT[1] )
READ aka SELECT (see above) DT[ , .( cols )] irisDT[ , .(Species, Sepal.Length)]
UPDATE / ADD column DT[ , a := b ] irisDT[ , Sepal.Area := Sepal.Width * Sepal.Length]
UPDATE / ADD multiple columns DT[ , `:=`(a = b, c = d) ] irisDT[ , `:=`(CreatedDate = Sys.Date(), User = "Steph")]
UPDATE / ADD multiple columns by reference DT[ , (newcols):=vals ] irisDT[ , c("a","b"):=.(1,2)]
DELETE DT <- DT[!condition] irisDT <- irisDT[!(Species=="setosa" & Petal.Length>=1.5)]

* Uses irisDT <- data.table(iris)

Metadata

Task Generic syntax Example(s)*
Structure str(DT) str(irisDT)
Column Names colnames(DT) colnames(irisDT)
Summary stats summary(DT) summary(irisDT)
Retrieve primary key info key(DT) key(irisDT)

* Uses irisDT <- data.table(iris)

Relationships

Task Generic syntax Example(s)*
INNER JOIN Y[X, nomatch=0L] lookupDT[irisDT,nomatch=0]
LEFT JOIN Y[X] lookupDT[irisDT]
FULL JOIN merge(X, Y, all=TRUE) merge(irisDT, lookupDT, all=TRUE)
CROSS JOIN optiRum::CJ.dt(X,Y) CJ.dt(irisDT, lookupDT)
UNION ALL rbindlist( list(X,Y), fill=TRUE ) rbindlist( list(irisDT, lookupDT), fill=TRUE )
UNION unique( rbindlist( list(X,Y), fill=TRUE ) ) unique( rbindlist( list(irisDT, lookupDT), fill=TRUE ) )
JOIN and AGGREGATE Y[X, cols, by] lookupDT[irisDT,.(count=.N),by=Band]

* Uses:

irisDT   <- data.table(iris, key="Species")
lookupDT <- data.table(Species=c("setosa", "virginica", "Blah"), Band=c("A", "B", "A"), key="Species")

Intermediate tasks

Task Generic syntax Example(s)*
UPDATE / ADD column of summary stat DT[ , a := b ] irisDT[ , All.SL.Mean:=mean(Sepal.Length)]
UPDATE / ADD column by group DT[ , a := b, by] irisDT[ , Species.SL.Mean:=mean(Sepal.Length), by=Species]
TOP by group DT[ , head(.SD), by] irisDT[ , head(.SD,2) , by=Species]
Largest record DT[ which.max(col) ] irisDT[ which.max(Sepal.Length) ]
Largest record by group DT[ , .SD[ which.max(col) ], by] irisDT[ , .SD[ which.max(Sepal.Length) ], by=Species]
Cumulative total DT[ , cumsum(col) ] irisDT[ , cumsum(Sepal.Width)]
NEGATIVE SELECT DT[ , .SD, .SDcols=-“colname”] irisDT[ , .SD, .SDcols=-"Species"]
RANK DT[ , frank(col) ] irisDT[ , frank(Sepal.Length,ties.method="first")]
AGGREGATE multiple columns DT[ , lapply(.SD, sum)] irisDT[ , lapply(.SD,sum), .SDcols=-"Species"]
AGGREGATE multiple columns by group DT[ , lapply(.SD, sum), by] irisDT[ , lapply(.SD,sum), by=Species]
COUNT DISTINCT multiple columns by group DT[ , lapply(.SD, uniqueN), by] irisDT[ , lapply(.SD,uniqueN), by=Species]
COUNT NULL multiple columns by group DT[ , lapply(.SD, function(x) sum(is.na(x))), by] irisDT[ , lapply(.SD,function(x) sum(is.na(x))), by=Species]
PIVOT data - to single value column melt(DT,…) melt(irisDT)
PIVOT data - to aggregate dcast(DT, a~b, function) dcast(melt(irisDT), Species ~ variable, sum)

* Uses irisDT <- data.table(iris)

Advanced tasks

Task Generic syntax Example(s)*
GROUP BY each new incidence of group DT[ , cols , by=(col, rleid(col))] irisDT[order(Sepal.Length), .N, by=.(Species, rleid(Species))]
Calculate using (previous/next/n) row DT[ , col / shift( cols, n, fill, type)] irisDT[ , prev.Sepal.Length:=shift(Sepal.Length), by=Species ]

* Uses irisDT <- data.table(iris)

Making charts

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DISCLAIMER

This intro covers the charting package ggplot2.

The “base” charting functionality will not be covered because it’s much more difficult to achieve good looking results quickly and I don’t believe in that much effort for so little benefit!

Requirements

  • necessary package: ggplot2
  • suggested package: RColorBrewer
  • suggested package: ggthemes
  • suggested package: optiRum

ggplot2

ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.

Glossary

Term Explanation Example(s)
plot A plot using the grammar of graphics ggplot()
aesthetics attributes of the chart colour, x, y
mapping relating a column in your data to an aesthetic
statistical transformation a translation of the raw data into a refined summary stat_density()
geometry the display of aesthetics geom_line(), geom_bar()
scale the range of values axes, legends
coordinate system how geometries get laid out coord_flip()
facet a means of subsetting the chart facet_grid()
theme display properties theme_minimal()

Constructing a chart - a step by step process

  1. Create the base plot (doesn’t work on it’s own)
library(ggplot2)

p <- ggplot(data=iris)
  1. Add aesthetic mappings (doesn’t work on it’s own)
p <- ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length, colour=Species))
  1. Add a geometry
p <- p + geom_point()
p

  1. (Optional) Add a statistic
p <- p + stat_boxplot(fill="transparent")
p
## Warning: position_dodge requires constant width: output may be incorrect
## Warning: position_dodge requires non-overlapping x intervals

  1. (Optional) Alter coordinate system
p <- p + coord_flip()
p
## Warning: position_dodge requires constant width: output may be incorrect
## Warning: position_dodge requires non-overlapping x intervals

  1. (Optional) Facet the chart
p <- p + facet_grid(.~Species)
p

  1. (Optional) Amend look and feel
p <- p + optiRum::theme_optimum()
p

Constructing a chart - a one-step process

ggplot(data=iris, aes(x=Sepal.Width, y=Sepal.Length, colour=Species)) + 
  geom_point() +
  stat_boxplot(fill="transparent") +
  # coord_flip() + # Commented out
  facet_grid(.~Species) +
  optiRum::theme_optimum()

More resources

Making documents

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Requirements

  • necessary package: knitr
  • necessary package: rmarkdown
  • necessary software: pandoc
  • recommended software: MiKTeX

Using R for documents

Producing documents / documentation directly in R means that you closely interweave (knit) your analysis and R code together. This reduces rework time when you want to change or extend your code, it reduces time to produce new versions, and because it’s code it’s easier to apply strong software development principles to it.

Oh, and you don’t need to spend hours making text boxes in powerpoint! Win ;-)

There are two languages which you can knit your r code into:

  • markdown
  • LaTeX (pronounced lay-tech alas)

Markdown is great for very quick generation and light (or css driven) styling and is what this section focusses on. LaTeX is excellent for producing stunning, more flexible documents.

rmarkdown standard text

The following text is the default text that gets created when you produce a new rmarkdown file in rstudio

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

rmarkdown standard documentation

The following text is part of the standard documentation on rmarkdown. I pull it from github.com/rstudio/rmarkdown and integrate it using knitr. It is better than I could produce and the act of integrating it gives an extra example of useful ways to build documents.

This document provides quick references to the most commonly used R Markdown syntax. See the following articles for more in-depth treatment of all the capabilities of R Markdown:

Emphasis

*italic*   **bold**

_italic_   __bold__

Headers

# Header 1

## Header 2

### Header 3

Lists

Unordered List:

* Item 1
* Item 2
    + Item 2a
    + Item 2b

Ordered List:

1. Item 1
2. Item 2
3. Item 3
    + Item 3a
    + Item 3b

R Code Chunks

R code will be evaluated and printed

```{r}
summary(cars$dist)
summary(cars$speed)
```

Inline R Code

There were 50 cars studied

Images

Images on the web or local files in the same directory:

![alt text](http://example.com/logo.png)

![alt text](figures/img.png)

Blockquotes

A friend once said:

> It's always better to give
> than to receive.

Plain Code Blocks

Plain code blocks are displayed in a fixed-width font but not evaulated

```
This text is displayed verbatim / preformatted
```

Inline Code

We defined the `add` function to
compute the sum of two numbers.
LaTeX Equations

LaTeX Equations

Inline equation:

$equation$

Display equation:

$$ equation $$

Horizontal Rule / Page Break

Three or more asterisks or dashes:

******

------

Tables

First Header  | Second Header
------------- | -------------
Content Cell  | Content Cell
Content Cell  | Content Cell
Reference Style Links and Images

Manual Line Breaks

End a line with two or more spaces:

Roses are red,
Violets are blue.

Miscellaneous

superscript^2^

~~strikethrough~~

Making reports

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What’s Shiny?

  • An interactive report framework
  • An R package that is available free (as is the server edition)
  • Uses “modern web standards” like bootstrap under the hood

Quick example

library(data.table)
library(shiny)
defaultdisplay<-list(
    width="100%", height="75%"
  )
shinyAppDir(
  system.file("examples/06_tabsets", package="shiny"),
  options = defaultdisplay
)

Shiny structure

Typical Contents

A shiny application report consists of two functions:

  • shinyServer()
  • shinyUI()

One says what to execute and the other states how to present it. Do all data manipulation, chart production in shinyServer()

“Lite” Contents

defaultdisplay<-list(width="100%", height="75%")

shinyApp(
    ui      = fluidPage(),
  , server  = function(input, output) {}
  , options = defaultdisplay
)

Files

You typically split into two files:

  • server.R containing shinyServer()
  • ui.R containing shinyUI()

This can then be run with runApp()

You can do a single file example app.R which contains both functions but this is typically better for very short apps.

Front-end layout

Use these just inside shinyUI() to produce a layout

##       Page Types
## 1:     basicPage
## 2: bootstrapPage
## 3:     fixedPage
## 4:     fluidPage
## 5:    navbarPage

Typical Inputs

Dates

shinyApp(
  ui = fluidPage(dateInput("datePicker", "Pick a date:", 
                           format="dd/mm/yy"),
                 dateRangeInput("dateRange", "Pick dates:", 
                                start=Sys.Date(), 
                                end=Sys.Date() ) ),
  server = function(input, output) {}
  ,options = defaultdisplay
)

Values

Basic

shinyApp(
  ui = fluidPage(numericInput("vals", "Insert a number:", 
                              value=15, min=10)  ),
  server = function(input, output) {}
  ,options = defaultdisplay
)

Sliders

shinyApp(
  ui = fluidPage(sliderInput("vals", "Insert a number:", 
                             min=0, max=50, value=15)  ),
  server = function(input, output) {}
  ,options = defaultdisplay
)

Text

A single line

shinyApp(
  ui = fluidPage(textInput("char", "Insert text:")  ),
  server = function(input, output) {}
  ,options = defaultdisplay
)

A paragraph

shinyApp(
  ui = fluidPage(tags$textarea(id="charbox", rows=3, 
                               cols=40, "Default value")  ),
  server = function(input, output) {}
  ,options = defaultdisplay
)

Selectors

shinyApp(
  ui = fluidPage(selectInput("multiselect", "Pick favourites:",
                             c("Green","Red","Blue"),
                             multiple=TRUE)  ),
  server = function(input, output) {}
  ,options = defaultdisplay
)

List of input types

##               Input controls
##  1:       checkboxGroupInput
##  2:            checkboxInput
##  3:                dateInput
##  4:           dateRangeInput
##  5:                fileInput
##  6:             numericInput
##  7:            passwordInput
##  8:     registerInputHandler
##  9:       removeInputHandler
## 10:              selectInput
## 11:           selectizeInput
## 12:              sliderInput
## 13:                textInput
## 14: updateCheckboxGroupInput
## 15:      updateCheckboxInput
## 16:          updateDateInput
## 17:     updateDateRangeInput
## 18:       updateNumericInput
## 19:        updateSelectInput
## 20:     updateSelectizeInput
## 21:        updateSliderInput
## 22:          updateTextInput
##               Input controls

Typical Outputs

Input values

shinyApp(
  ui = fluidPage(textInput("char", "Insert text:") ,
                 textOutput("text")  ),
  server = function(input, output) {
    output$text <- renderText(input$char)
  }  ,options = defaultdisplay
)

Basic tables

shinyApp(
  ui = fluidPage(tableOutput("basictable")  ),
  server = function(input, output) {
    output$basictable <- renderTable(head(iris,5))
  }  ,options = defaultdisplay
)

Interactive tables

shinyApp(
  ui = fluidPage(dataTableOutput("datatable")  ),
  server = function(input, output) {
    output$datatable <- renderDataTable(head(iris,5))
  }  ,options = defaultdisplay
)

Charts

shinyApp(
  ui = fluidPage(plotOutput("chart")  ),
  server = function(input, output) {
    output$chart <- renderPlot(pairs(iris))
  }  ,options = defaultdisplay
)

Reactivity

Simple reactivity

  • Make functions that process inputs only when they change
a <- reactive({input$a})
a
  • Use these reactive functions in downstream server items for DRY & to reduce processing effort

An Example

shinyApp(
  ui = fluidPage(textInput("char", "Insert text:") ,
                 textOutput("textA"),textOutput("textB") ),
  server = function(input, output) {
    char<-reactive({rep(input$char,5)})
    output$textA <- renderText(paste(char(),collapse="+"))
    output$textB <- renderText(paste(char(),collapse="-"))
  }
  ,options = defaultdisplay
)

Styling

shinythemes

  • Get a different look and feel with the package shinythemes
  • Uses a number of bootstrap based themes
  • Good-looking quickly, but of course not company branded
  • View themes at bootswatch.com

CSS

  • Shiny outputs html so you can write CSS that works with it
  • Full list of CSS items doesn’t exist, use F12 on chrome or check out selectorgadget via rvest
  • Simple stuff like body, h1 will all work

shinydashboards

Infrastructure

Ad-hoc shiny

  • Rstudio (easiest) or just run directly
  • Use shiny::runApp()
  • Great for “expert” use

Cloud

  • shinyApps.io
  • Deploy with shinyApps package
  • Free for light use
  • Extra management features at increased costs
  • Great for hands-off management

Central server

  • shiny-server
  • Runs on linux
  • Free community edition
  • Extra management features & LDAP auth in Pro Edition (but can roll your own)
  • Great for sensitive and/or db-driven appplications

Making diagrams

LaTeX

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About LaTeX

Pronunciation: lay-tech

LaTeX is an open source markup language with a typesetting engine. It’s been around since the 70s and generally makes awesome documents.

LaTeX is designed to work stand-alone, or integrate with other languages. It’s particularly good with R.

It works in the way rmarkdown does with knitr, but allows for more sophisticated document styling.

Why LaTeX?

  • Seamless integration between code and content
  • Fantastic looking documentation
  • Works on all operating systems and has a number of cloud/internet hosted services
  • Can use markdown and append LaTeX style
  • Use for more polished presentations without having to go into PowerPoint

Get LaTeX

Once you have a LaTeX installation, you can write LaTeX in Rstudio.

In Rstudio, select the File type “R Sweave” which saves as a .Rnw file.

Learn LaTeX

LaTeX is a very deep language, so no attempt is made to teach you here.

One of the easiest ways of getting started with LaTeX (generally) is to pick one of the examples / templates on overleaf.com and play with it.

For using R and LaTeX, you can use the minimal examples on yihui.name/knitr to get started.

See the LaTeX wikibook for lots of info about LaTeX.

Source control - theory

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version control all the things

SOURCE CONTROL IS FOR ALL THE THINGS

Fundamentals

Source control is important because it provides:

  • a backup system for your code
  • a means of tracking why you made changes
  • a way of sharing code
  • a system for multiple people to work safely
  • another potential way to deploy code manually
  • options for automated testing and deployment
  • audit trails

There are two types of source control systems:

  • centralised
  • distributed

Centralised means that there is a single storage location and to work on a file it must be exclusively checked out. Distributed systems involve taking copies of the code base, making changes, and pushing these back to primary storage location.

Both have their own disadvantages but since with distributed source control you never get the situation where someone’s left a file checked out as they go on holiday and no-one else can use it, I’m a big fan of distributed source control systems.

Git

Git is a distributed source control system.

It integrates neatly into Rstudio, making it easy to source control your analysis.

The git conceptual model (@cthydng)

git model

Git glossary

  • Repository: Your project and it’s history
  • Remote: The (TFS/GitHub/VSO etc) server
  • Clone:Take a linked copy of a repository
  • Fork: Take an unlinked copy of a repository
  • Pull: Update your copy of a repository
  • Add: Include a file in a change
  • Commit: Make the latest state of all the added files the new default state
  • Push: Update the central copy of the repository with your commits

There are more terms. For a friendly glossary see Github’s git glossary, and for an extensive, technical glossary see the official Git glossary

Source control - tfsR

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## Loading required package: devtools
## Loading required package: tfsR

TFS

In TFS2013 and Visual Studio Online, you can use git repositories inside TFS. This gives the ability to leverage all the advantages of a distributed source control system whilst still giving you all the functionality of TFS.

If you’re using SourceSafe or earlier editions of TFS, UPGRADE for everyone’s sakes.

If you’re not using any source control, consider getting Visual Studio Online as it’s free for 5 users (plus unlimited MSDN users), requires no hosting or maintenance on your part, and has fine grained permissions management.

Working with your TFS site in R with tfsR

If you have/want to work with R using git repositories in TFS (either on-premises or via Visual Studio Online), tfsR saves you having to have Visual Studio (installed on your machine or online), and allows you to directly create git repositories within TFS.

tfsR Information

Setup

You must have a username (often an email address or AD account) and password for connecting. That’s basically it!

Intended use

Since you need to provide credentials to these functions, it is anticipated that you would use these on an ad-hoc basis in much the same way you would use devtools.

It’s a great way to quickly setup a repository if you need to get your stuff into source control.

In terms of actually using your repositories once they’re created, I recommend git2r.

Limitations

  • The active components use a Team Project with Git selected as the engine for it
  • Due to the lack of an API call for creating new projects, it is anticipated that you use this to create repositories within a “parent” or container project.
  • If you need seperate backlogs etc, you’ll have to create new Projects in the GUI
  • This only works currently with basic authentication, OAuth2.0 may follow
  • getTFSProjects will only handle a single TFS URL at a time (httr restriction)
  • createTFSRepository will only handle a single repository at a time, to maintain consistency

Using tfsR

Get info on existing repositories

A great place to start is a) verifying you can connect to your TFS and b) see what projects you could create repositories under

library(tfsR)

tfs          <-"https://stefflocke.visualstudio.com"
authcreds    <-httr::authenticate("tfsexample","UsedForExampl3s")
repositories <-getTFSProjects(tfs,authcreds)

knitr::kable(repositories)
id name url remoteUrl project.id project.name project.url project.state type
ef220f61-3654-4f00-a946-9faf3eb76c52 GitRepoContainer https://stefflocke.visualstudio.com/DefaultCollection/_apis/git/repositories/ef220f61-3654-4f00-a946-9faf3eb76c52 https://stefflocke.visualstudio.com/DefaultCollection/_git/GitRepoContainer f8c183a2-cbd1-43bb-a124-279dc31f7fe5 GitRepoContainer https://stefflocke.visualstudio.com/DefaultCollection/_apis/projects/f8c183a2-cbd1-43bb-a124-279dc31f7fe5 wellFormed Project
d72180e6-bcf1-4061-a7eb-fc2a1722b852 APItest https://stefflocke.visualstudio.com/DefaultCollection/_apis/git/repositories/d72180e6-bcf1-4061-a7eb-fc2a1722b852 https://stefflocke.visualstudio.com/DefaultCollection/GitRepoContainer/_git/APItest f8c183a2-cbd1-43bb-a124-279dc31f7fe5 GitRepoContainer https://stefflocke.visualstudio.com/DefaultCollection/_apis/projects/f8c183a2-cbd1-43bb-a124-279dc31f7fe5 wellFormed Sub-Project

Create a new repository

TFS doesn’t allow you to create new projects so if you want to create a new repository, you have to do it in an existing project or go to the GUI and manually create one (there is no API call for this).

You don’t want to start nesting repositories so you need to make the repository under a top-level project. As such, in this bit of functionality, you provide the name of the top-level project which you want to put your repository, and it goes away and gets a GUID if the project exists and is a top-level project, then tells the API to create a repository in it.

tfs          <-"https://stefflocke.visualstudio.com"
authcreds    <-httr::authenticate("tfsexample","UsedForExampl3s")
parentproj   <-"GitRepoContainer"
newrepo      <- as.character(random::randomStrings(n=1, len=6))
createdrepo  <- createTFSRepository(tfs,authcreds,parentproj,newrepo)

This will provide you with the URL you need to be able to push and pull commits to. In this case it has created a repository called “iuTKAQ” which can be interacted with at the URL https://stefflocke.visualstudio.com/DefaultCollection/GitRepoContainer/_git/iuTKAQ

Source control - git2r

Testing

Package development

Bonus round: Continuous integration with Travis-CI

Linux basics

Installing Rstudio

Installing shiny-server

Server creation

Azure portal, using gallery creation for VM quickcreate quickcreate

Configuring the VM

  1. Get PuTTY
  2. Connect to your VM via the public IP quickcreate
  3. Use the login details in the creation wizard. The password won’t look like you’re typing
  4. Run sudo apt-get update to get the package repository metadata
  5. Run sudo apt-get install r-base to get R. Will have lots of extra associated packages - select Y when prompted
  6. Follow the installation instructions, using the latest file
    • Run sudo su - -c "R -e \"install.packages('shiny', repos='http://cran.rstudio.com/')\"" to install shiny in R
    • Run sudo apt-get install gdebi-core to enable processing of shiny-server installation package
    • Run (latest version of) wget http://download3.rstudio.org/ubuntu-12.04/x86_64/shiny-server-1.3.0.403-amd64.deb
    • Run (latest version of) sudo gdebi shiny-server-1.3.0.403-amd64.deb

Configuring port (away from 3838) and allowing on Azure

  1. Change shiny-server to run on port 80 by amending port in conf file sudo nano /etc/shiny-server/shiny-server.conf
  2. Restart shiny-server to apply port change sudo restart shiny-server
  3. Add port 80 to Azure endpoints for the VM port specify shiny-server first view

Bonus round: AD authentication